MaskedSVM_SDMs
Load packages and source functions
library(raster)## Loading required package: spsource("/Users/hellenfellows/OneDrive\ -\ AMNH/Wallace/maskRangeR/R/svm.R")Load occurrence data and SDMs
I loaded the occurrence data for the three species, with latitude and longitude points coming from literature.
variegatus <- read.csv("~/OneDrive - AMNH/Wallace/Occurrence_Data/Bradypus_variegatus_litdata.csv")
head(variegatus)## name longitude latitude source
## 1 bradypus_variegatus -46.73330 -23.45000 Anderson and Handley 2001
## 2 bradypus_variegatus -44.71667 -23.21667 de Moraes-Barros 2011
## 3 bradypus_variegatus -46.61667 -20.71667 de Moraes-Barros 2011
## 4 bradypus_variegatus -40.06670 -19.33330 Anderson and Handley 2001
## 5 bradypus_variegatus -63.16670 -17.80000 Anderson and Handley 2001
## 6 bradypus_variegatus -63.66670 -17.45000 Anderson and Handley 2001
## geographic_region management_unit population_region
## 1 NA
## 2 1 1+5
## 3 2 2
## 4 NA
## 5 NA
## 6 NAtridactylus <- read.csv("~/OneDrive - AMNH/Wallace/Occurrence_Data/Bradypus_tridactylus_litdata.csv")
head(tridactylus)## taxon_name longitude latitude source
## 1 bradypus_tridactylus -59.50000 -1.833330 de Moraes-Barros 2011
## 2 bradypus_tridactylus -56.73333 -2.183333 de Moraes-Barros 2011
## 3 bradypus_tridactylus -58.68333 6.383333 de Moraes-Barros 2011
## 4 bradypus_tridactylus -59.50000 2.833333 de Moraes-Barros 2011
## 5 bradypus_tridactylus -55.16667 5.833333 de Moraes-Barros 2011
## 6 bradypus_tridactylus -63.55000 8.133333 de Moraes-Barros 2011torquatus <- read.csv("~/OneDrive - AMNH/Wallace/Occurrence_Data/Bradypus_torquatus_litdata.csv")
head(torquatus)## taxon_name longitude latitude source
## 1 bradypus_torquatus -39.40000 -15.83300 Moreira et al. 2014
## 2 bradypus_torquatus -39.11660 -14.80000 Moreira et al. 2014
## 3 bradypus_torquatus -39.12528 -14.65778 Moreira et al. 2014
## 4 bradypus_torquatus -39.28333 -14.83333 Moreira et al. 2014
## 5 bradypus_torquatus -38.05000 -12.50000 Moreira et al. 2014
## 6 bradypus_torquatus -39.04000 -13.65000 Moreira et al. 2014I used SDMs built from a 4-degree background buffer around points thinned to 40 km. Models were selected based on the lowest AIC and lowest omission rate.
#load SDMs
var_sdm <- raster("/Users/hellenfellows/OneDrive\ -\ AMNH/Wallace/Wallace_code/thinned_variegatus_L_1_cloglog.tif")
plot(var_sdm)
tri_sdm <- raster("/Users/hellenfellows/OneDrive\ -\ AMNH/Wallace/Wallace_code/thinned_tridactylus_LQ_2_cloglog.tif")
plot(tri_sdm)
tor_sdm <- raster("/Users/hellenfellows/OneDrive\ -\ AMNH/Wallace/Wallace_code/thinned_torquatus_H_3_cloglog.tif")
plot(tor_sdm)
Spatial SVMs
The following SVMs are built using only spatial information, i.e. the occurrence localities of the two species.
Unweighted spatial SVMs
In the unweighted SVM, the occurrences of each species are weighed equally.
# create SVM
unweighted_svm_occ <- rangeSVM(variegatus[,2:3], tridactylus[,2:3], torquatus[,2:3], nrep = 100)# use SVM to create a raster of predicted regions
sloth_unweighted_svm_occ <- rangeSVM_predict(svm = unweighted_svm_occ, r = var_sdm)
plot(sloth_unweighted_svm_occ)
points(variegatus[,2:3], pch = 20, cex = 0.75, col = "black")
points(tridactylus[,2:3], pch = 20, cex = 0.75, col = "red")
points(torquatus[,2:3], pch = 20, cex = 0.75, col = "blue")
The grey areas indicate regions predicted for B. variegatus, the yellow areas indicate regions predicted for B. tridactylus, and the green areas indicate regions predicted for B. torquatus. Black points show the occurrences of B. variegatus, red show B. tridactylus, and blue show B. torquatus.
B. variegatus
# masked SDM predictions for variegatus
var_unweighted_svm_occ <- sloth_unweighted_svm_occ == 1
var_unweighted_svm_occ[var_unweighted_svm_occ == 0] <- NA
var_unweighted_svm_occ_mask <- mask(var_sdm, var_unweighted_svm_occ)
plot(var_unweighted_svm_occ_mask)
B. tridactylus
# masked SDM predictions for tridactylus
tri_unweighted_svm_occ <- sloth_unweighted_svm_occ == 2
tri_unweighted_svm_occ[tri_unweighted_svm_occ == 0] <- NA
tri_unweighted_svm_occ_mask <- mask(tri_sdm, tri_unweighted_svm_occ)
plot(tri_unweighted_svm_occ_mask)
B. torquatus
# masked SDM predictions for torquatus
tor_unweighted_svm_occ <- sloth_unweighted_svm_occ == 3
tor_unweighted_svm_occ[tor_unweighted_svm_occ == 0] <- NA
tor_unweighted_svm_occ_mask <- mask(tor_sdm, tor_unweighted_svm_occ)
plot(tor_unweighted_svm_occ_mask)
Weighted spatial SVMs
For the weighted SVMs, I modified the code in the svm.R script of maskRangeR so that when weight = TRUE, the e1071::svm() function uses class.weights = "inverse". According to the e1071 documentation, this means the weights will be inversely proportional to the class distribution. We still need to discuss if this is the appropriate way to deal with weighting SVMs when there are more than 2 species.
# create SVM
weighted_svm_occ <- rangeSVM(variegatus[,2:3], tridactylus[,2:3], torquatus[,2:3], nrep = 100, weight = TRUE)# use SVM to create a raster of predicted regions
sloth_weighted_svm_occ <- rangeSVM_predict(svm = weighted_svm_occ, r = var_sdm)
plot(sloth_weighted_svm_occ)
points(variegatus[,2:3], pch = 20, cex = 0.75, col = "black")
points(tridactylus[,2:3], pch = 20, cex = 0.75, col = "red")
points(torquatus[,2:3], pch = 20, cex = 0.75, col = "blue")
B. variegatus
# masked SDM predictions for variegatus
var_weighted_svm_occ <- sloth_weighted_svm_occ == 1
var_weighted_svm_occ[var_weighted_svm_occ == 0] <- NA
var_weighted_svm_occ_mask <- mask(var_sdm, var_weighted_svm_occ)
plot(var_weighted_svm_occ_mask)
B. tridactylus
# masked SDM predictions for tridactylus
tri_weighted_svm_occ <- sloth_weighted_svm_occ == 2
tri_weighted_svm_occ[tri_weighted_svm_occ == 0] <- NA
tri_weighted_svm_occ_mask <- mask(tri_sdm, tri_weighted_svm_occ)
plot(tri_weighted_svm_occ_mask)
B. torquatus
# masked SDM predictions for torquatus
tor_weighted_svm_occ <- sloth_weighted_svm_occ == 3
tor_weighted_svm_occ[tor_weighted_svm_occ == 0] <- NA
tor_weighted_svm_occ_mask <- mask(tor_sdm, tor_weighted_svm_occ)
plot(tor_weighted_svm_occ_mask)
Hybrid SVMs
The hybrid SVMs are created using the habitat suitability predicted by the species distribution models in addition to spatial information, i.e. occurrences of the species.
Unweighted hybrid SVMs
# create SVM
unweighted_svm_hyb <- rangeSVM(variegatus[,2:3], tridactylus[,2:3], torquatus[,2:3], sdm = raster::stack(var_sdm, tri_sdm, tor_sdm), nrep = 100)sloth_unweighted_svm_hyb <- rangeSVM_predict(svm = unweighted_svm_hyb, r = var_sdm, sdm = raster::stack(var_sdm, tri_sdm, tor_sdm))
plot(sloth_unweighted_svm_hyb)
points(variegatus[,2:3], pch = 20, cex = 0.75, col = "black")
points(tridactylus[,2:3], pch = 20, cex = 0.75, col = "red")
points(torquatus[,2:3], pch = 20, cex = 0.75, col = "blue")
B. variegatus
# masked SDM predictions for variegatus
var_unweighted_svm_hyb <- sloth_unweighted_svm_hyb == 1
var_unweighted_svm_hyb[var_unweighted_svm_hyb == 0] <- NA
var_unweighted_svm_hyb_mask <- mask(var_sdm, var_unweighted_svm_hyb)
plot(var_unweighted_svm_hyb_mask)
B. tridactylus
# masked SDM predictions for tridactylus
tri_unweighted_svm_hyb <- sloth_unweighted_svm_hyb == 2
tri_unweighted_svm_hyb[tri_unweighted_svm_hyb == 0] <- NA
tri_unweighted_svm_hyb_mask <- mask(tri_sdm, tri_unweighted_svm_hyb)
plot(tri_unweighted_svm_hyb_mask)
B. torquatus
# masked SDM predictions for torquatus
tor_unweighted_svm_hyb <- sloth_unweighted_svm_hyb == 3
tor_unweighted_svm_hyb[tor_unweighted_svm_hyb == 0] <- NA
tor_unweighted_svm_hyb_mask <- mask(tor_sdm, tor_unweighted_svm_hyb)
plot(tor_unweighted_svm_hyb_mask)
Weighted hybrid SVMs
# create SVM
weighted_svm_hyb <- rangeSVM(variegatus[,2:3], tridactylus[,2:3], torquatus[,2:3], sdm = raster::stack(var_sdm, tri_sdm, tor_sdm), nrep = 100, weight = TRUE)sloth_weighted_svm_hyb <- rangeSVM_predict(svm = weighted_svm_hyb, r = var_sdm, sdm = raster::stack(var_sdm, tri_sdm, tor_sdm))
plot(sloth_weighted_svm_hyb)
points(variegatus[,2:3], pch = 20, cex = 0.75, col = "black")
points(tridactylus[,2:3], pch = 20, cex = 0.75, col = "red")
points(torquatus[,2:3], pch = 20, cex = 0.75, col = "blue")
B. variegatus
# masked SDM predictions for variegatus
var_weighted_svm_hyb <- sloth_weighted_svm_hyb == 1
var_weighted_svm_hyb[var_weighted_svm_hyb == 0] <- NA
var_weighted_svm_hyb_mask <- mask(var_sdm, var_weighted_svm_hyb)
plot(var_weighted_svm_hyb_mask)
B. tridactylus
# masked SDM predictions for tridactylus
tri_weighted_svm_hyb <- sloth_weighted_svm_hyb == 2
tri_weighted_svm_hyb[tri_weighted_svm_hyb == 0] <- NA
tri_weighted_svm_hyb_mask <- mask(tri_sdm, tri_weighted_svm_hyb)
plot(tri_weighted_svm_hyb_mask)
B. torquatus
# masked SDM predictions for torquatus
tor_weighted_svm_hyb <- sloth_weighted_svm_hyb == 3
tor_weighted_svm_hyb[tor_weighted_svm_hyb == 0] <- NA
tor_weighted_svm_hyb_mask <- mask(tor_sdm, tor_weighted_svm_hyb)
plot(tor_weighted_svm_hyb_mask)